I think your proposal of modifying plot.ts() to allow 'log' to be
vectorized would be the most natural solution here.
For what it's worth, the details of the implementation and the fact
that you can supply a panel function allows an ugly hack:
pfun <- function(...) {
e <- parent.frame()
Dear Sir or Madam
I’m trying to compile a collection of datasets that require use of the
following hypothesis tests.
Are there datasets within the R library that I can get access to?
Kind regards
James Carrigan
Hypothesis Testing
t.test(X,Y)
— performs a two sample t-test between X and Y
Gracias!!
El 31/5/23, Javier Marcuzzi escribió:
> Estimados
>
> José envio un archivo, pero no es privativo. Hay varios programas para
> abrir un documento word, pero, dicho de otra forma, es docx, especificado
> en:
>
> https://es.wikipedia.org/wiki/Office_Open_XML
>
> Bajo esas
De acuerdo a Bard;
• scale_colour_viridis_d: Esta función utiliza la paleta de colores
"viridis" en una escala continua, donde los colores varían de forma suave y
continua. Es adecuada para representar datos en los que se desea resaltar las
variaciones sutiles en los valores de la
Hola Javier,
d = discrete
c = continuous
b = binary
Manuel
On Wed, 31 May 2023 at 14:51 Javier Gómez Gonzalez
wrote:
> Hola a todos:
>
> Alguién me podría explicar cuales son las diferencias entre las diferentes
> escalas de viridis en ggplot2, es decir en qué se diferencian
>
dear members,
I am using arfima() from forecast package to model
a time series. The following is the code:
> LYGH[[202]]
[1] 45.40 3.25 6.50 2.15
> arfima(LYGH[[202]])
Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma, hess = hess, fdf.work =
fdf$w) :
NA/NaN/Inf
Hola a todos:
Alguién me podría explicar cuales son las diferencias entre las diferentes
escalas de viridis en ggplot2, es decir en qué se diferencian
scale-colour_viridis_d de scale_colour_viridis_c, scale_colour_viridis_b.
Y cual es la diferencia entre scale-fiil-viridis_d de
I will try to explain in a bit more detail.
1. I want to have the labels on the right with a small size to be able
to read them
2. I would like not to have the labels on the left
3. I would like to be able to choose the number of principal ticks for
every sensor. That is to say the value of
Buenas, José:
En una lista de software libre es preferible no usar formatos privativos de
archivos como los que utiliza Microsoft.
Las instrucciones siguientes hacen un gráfico de barras:
x <- rbinom(10, 20, .3)
barplot(table(x))
¿Los valores no se corresponden con una variable cuantitativa
On 5/31/23 2:12 PM, Viechtbauer, Wolfgang (NP) wrote:
How about using the same 'mar' for all plots, but adding an outer margin?
DAX <- EuStockMarkets[, 'DAX']
DAX. <- cbind(DAX, diff(log(DAX)), diff(diff(log(DAX
colnames(DAX.) <- c("DAX", 'vel (%)', 'accel (%)')
head(DAX.)
How about using the same 'mar' for all plots, but adding an outer margin?
DAX <- EuStockMarkets[, 'DAX']
DAX. <- cbind(DAX, diff(log(DAX)), diff(diff(log(DAX
colnames(DAX.) <- c("DAX", 'vel (%)', 'accel (%)')
head(DAX.)
par(mfrow=c(3,1), mar=c(1,4.5,0,2), oma=c(3,0,1,0))
plot(DAX.[, 1],
Estimados
mi pregunta viene en archivo adjunto
saludos
José
R pregunta.docx
Description: MS-Word 2007 document
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On 5/31/23 9:20 AM, Eric Berger wrote:
I sent you an updated response to deal with the redundant copies of the x-axis.
Re-sending.
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
I got that.
I sent you an updated response to deal with the redundant copies of the x-axis.
Re-sending.
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
On Wed, May 31, 2023 at 4:27 PM Spencer Graves
wrote:
>
>
>
>
On 5/30/23 10:23 AM, Eric Berger wrote:
What if you just precede these commands as follows:
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX')
plot(DAX.[, 2], ylab='vel (%)')
plot(DAX.[, 3], ylab='accel (%)')
Most of the space is consumed with two extraneous copies of the axis.
We
Slight modification to have the xaxt ticks and labels only appear on
the bottom chart
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
On Tue, May 30, 2023 at 6:23 PM Eric Berger wrote:
>
> What if you
Hello,
I've been enjoying using the "Mixture and Hidden Markov Models in R" by Visser
& Speekenbrink to learn how to apply these analyses to my own data using
depmixS4.
I currently have a fitted 4-state mixture model with three emissions variables
and one binomial covariate (HS). I am trying
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